1. Technical Filed
The invention relates generally to marketing methods, and more particularly, to a method, system, and program product for increasing the effectiveness of customer contact strategies.
2. Background Art
Direct marketing involves advertising to customers at a location other than the point of sale. Catalogs, first-class mail, telemarketing, and e-mail are some examples of direct marketing techniques that are currently utilized to promote the sale of goods or services.
Increasingly, retail companies are adding direct marketing to their mix of marketing techniques. In addition, with the explosion of the Internet and e-commerce, consumers are presented with increasingly attractive alternatives to mail for the direct purchase of goods and services in their homes.
In response to these changes, direct marketers have responded in a variety of ways. Many direct marketers have improved their targeting of recipients of direct marketing through automation. For example, automation has been achieved by programming computers to perform sophisticated statistical analysis and modeling, develop marketing databases, increase the sophistication of their predictive models, or enhance their current processes with leading edge marketing tools such as data mining. While these efforts have helped reduce the negative impact of the changing marketing atmosphere, the industry has not been able to improve the average response rate to direct marketing.
A commonly-used marketing technique is called the RFM (Recency, Frequency and Monetary Value) technique. U.S. Pat. No. 6,925,441 B1 to Jones, III et al., incorporated herein by reference, discloses a computer-implemented targeted marketing system which evaluates many factors, including the RFM factors, to determine a customer list to be used for sending marketing materials in connection with a single proposed promotion event. The RFM technique is based on the theory that the customers that are most likely to respond to a proposed direct marketing event (e.g., a mailing of an offer) are those that have most recently been customers (Recency), and that have frequently been repeat customers (Frequency), and that have purchased significant dollar amounts (Monetary Value). Existing customers are scored based on their characteristics related to each of these three criteria, and a customer with a high RFM score is considered a good target for the proposed marketing event under analysis. Based on the RFM scores, a specialized customer list is generated for a single proposed marketing event.
More recently, U.S. Pat. No. 6,567,786 to Bibelnieks et al., which is hereby incorporated herein by reference, describes a system and method for increasing the effectiveness of customer contact strategies. More specifically, Bibelnieks et al. describe an approach that focuses on an individual customer rather than a particular promotion event.
For example, referring to FIG. 1, a matrix depicting the “horizontal marketing” approach of Bibelnieks et al. is shown. Individual customers or groups of customers, C1-C5, are shown in the first column and past, current, and future promotion events, P−n, P−1, P, P+1, P+n, are shown in the first row. A “1” indicates that the customer will be included in a particular promotion event and a “0” indicates that the customer will not be included in the promotion event, based on some predetermined value threshold. A value threshold may be global, or, more likely, will be specific to an individual promotion event and based, in part, on the costs associated with the event.
The decision of whether to include a particular customer or customer group in a promotion event is based on the expected value in including the customer or customer group in the event and the event's threshold. The Bibelnieks et al. approach calculates a value for each promotion event relative to each customer or customer group. While earlier approaches utilized a two-dimensional matrix expressed as V[customer, event], Bibelnieks et al. reduce this to an array expressed as V[customer * event]. Hereinafter, a promotion event applied to a particular customer or customer group is referred to as a customer event.
Under the Bibelnieks et al. approach, the decision whether to include a particular customer or customer group on a promotion event also considers the “cannibalistic” effect of one promotion event on another promotion event. This represents a significant improvement over earlier approaches. Referring to FIG. 2, a matrix depicting the “cannibalistic” effect of one promotion event on another is shown. Individual promotion events are shown in the first column and row. Accordingly, the main diagonal of the matrix plots a promotion event against itself and is therefore inapplicable. As shown, 40% of the value of promotion P would be cannibalized by promotion P+1. In turn, 50% of the value of promotion P+1 will be cannibalized by promotion P. Cannibalization may consider one or more of a merchandising component (i.e., the type of goods), a promotion-type component (e.g., promotions having similar incentives), and a time component (i.e., the time between promotions).
The Bibelnieks et al. approach also considers the “saturation” effect of one promotion event on another promotion event, relative to a particular customer. FIG. 3 shows a matrix of a “saturation” effect. A value of “1” indicates that the inclusion of the customer in the promotion event shown in the first column has no impact on the value of including the customer in the promotion event shown in the first row. For example, including a customer in promotion event P will have no impact on the value of also including that customer in promotion event P+2. Contrarily, a value of “0” indicates that once a customer has been included in the promotion event of the first column, there is no value in including that customer in the promotion event shown in the first row. For example, there is no value in including a customer in promotion event P+1 if that customer has previously been included in promotion event P. In some cases, a saturation effect is simply an extreme example of a cannibalistic effect (e.g., a very high or very low cannibalistic effect results in a “0” or a “1,” respectively, in the matrix of FIG. 3). In other cases, the saturation effect may represent broader or narrower considerations relevant to a decision whether to include a customer in a promotion event. For example, where two promotion events involve identical merchandise, and that merchandise is generally an infrequent purchase (e.g., an automobile), the saturation effect is likely to be high (i.e., a “0” in the saturation matrix), even if the cannibalistic effect is relatively low (e.g., the promotion events do not overlap temporally).
Despite these improvements over earlier approaches, Bibelnieks et al. do not teach an efficient method for ensuring that the most valuable customer-specific promotion event is chosen. That is, upon the determination of the most valuable promotion event for a particular customer and inclusion of that customer in the promotion event, the Bibelnieks et al. approach requires a complete re-evaluation of the remaining promotion events, including the recalculation of their cannibalistic and saturation effects. This is computationally expensive to the point of being impractical when a large number of promotion events and/or customers must be evaluated.
To this extent, a need exists for a method for increasing the effectiveness of customer contact strategies that does not suffer from the deficiencies above.